Quality control and improvement for multistage systems: A survey

IIE Transactions (Institute of Industrial Engineers)

Volumn 41, Issue 9, 2009, Pages 744-753

  Shi, Jianjun ^a   Zhou, Shiyu ^b  

^a Georgia Institute of Technology   (United States)

^b University of Wisconsin Madison   (United States)

Author keywords

Multistage systems; Quality engineering; Variation modeling and reduction

Indexed keywords

COMPLEX INTERACTION; DESIGN OPTIMIZATION; LOCAL VARIATIONS; MULTIPLE COMPONENTS; MULTISTAGE SYSTEM; MULTISTAGE SYSTEMS; QUALITY CHARACTERISTIC; QUALITY ENGINEERING; SERVICE SYSTEMS;

OPTIMIZATION; QUALITY ASSURANCE; QUALITY FUNCTION DEPLOYMENT; SURVEYS; TOTAL QUALITY MANAGEMENT;

INSPECTION;

EID: 70849119723     PISSN: 0740817X     EISSN: 15458830     Source Type: Journal    
DOI: 10.1080/07408170902966344     Document Type: Article

References (91)

1. Agrawal, R., Lawless, J.F. andMackay, R.J. (1999) Analysis of variation transmission in manufacturing processes - part II. Journal of Quality Technology, 31(2), 143-154.
2. Apley, D.W. and Ding, Y. (2005) A characterization of diagnosability conditions for variance components analysis in assembly operations. IEEE Transactions on Automation Science and Engineering, 2(2), 101-110.
3. Apley,D.W. and Lee,H.Y. (2003) Identifying spatial variation patterns in multivariate manufacturing processes: A blind separation approach. Technometrics, 45(3), 220-234.
4. Apley,D.W. and Shi, J. (1998) Diagnosis ofmultiple fixture faults in panel assembly. ASME Transactions, Journal of Manufacturing Science and Engineering, 120(4), 793-801.
5. Apley, D.W. and Shi, J. (2001) A factor analysis method for diagnosing variability in multivariate manufacturing processes. Technometrics 43(1), 84-95.
6. Beswick, C.A. and Cravens, D.W. (1977) A multistage decision model for salesforce management. Journal of Marketing Research, 14(2 , 135-144.
7. Bolton, R.N. and Drew, J.H. (1991) A multistage model of customers' assessments of service quality and value. The Journal of Consumer Research, 17(4), 375-384
8. Camelio, J., Hu, S.J. and Ceglarek, D. (2003) Modeling variation propagation of multi-station assembly systems with compliant parts. ASME Transactions, Journal of Mechanical Design, 125(4), 673-681.
9. Ceglarek, D. and Shi, J. (1995) Dimensional variation reduction for automotive body assembly. Journal of Manufacturing Review, 8 2), 139-154.
10. Ceglarek, D. and Shi, J. (1996) Fixture failure diagnosis for autobody assembly using pattern recognition. ASME Transactions, Journal of Engineering for Industry, 188(1), 55-65.
11. Ceglarek, D., Shi, J. and Wu, S.M. (1994) A knowledge-based diagnosis approach for the launch of the auto-body assembly process. ASME Transactions, Journal of Engineering for Industry, 116(4), 491-499.
12. Chang, M. and Gossard, D.C. (1998) Computational method for diagnosis of variation-related assembly problem. International Journal of Production Research, 36(11), 2985-2995.
13. Chen, Y., Jin, J. and Shi, J. (2004) Integration of dimensional quality and locator reliability in design and evaluation of multi-station body-inwhite assembly processes. IIE Transactions, 39(9), 827-839.
14. Chen, Y. and Jin, J. (2005) Quality-reliability chain modeling for systemreliability analysis of complex manufacturing processes. IEEE Transactions on Reliability, 54(3), 475-488.
15. Chen,Y., Ding,Y., Jin, J. andCeglarek,D. (2006) Integration of tolerance and maintenance design for multi-station manufacturing processes. IEEE Transactions on Automation Science and Engineering, 3(4), 440-453.
16. Chen, N. and Zhou, S. (2009) Detectability study for statistical monitoring of multivariate dynamic processes. IIE Transactions, 41(7), 593-604.
17. Chinchilli, V.M. (1983) Estimates of sensitivity and specificity in a multistage screen for medical diagnosis. Biometrics, 39(2), 333-340.
18. Ding, Y., Ceglarek, D. and Shi, J. (2000) Modeling and diagnosis of multistage manufacturing processes: Part I state space model. Presented at the 2000 Japan/USA Symposium on Flexible Automation, July 23-26, Ann Arbor, MI.
19. Ding, Y., Ceglarek, D. and Shi, J. (2002a) Fault diagnosis of multistage manufacturing processes by using state space approach. ASME Transactions, Journal of Manufacturing Science and Engineering, 124(2), 313-322.
20. Ding, Y., Ceglarek, D. and Shi, J. (2002b) Design evaluation of multistation manufacturing processes by using state space approach. ASME Transactions, Journal of Mechanical Design, 124(4), 408-418.
21. Ding, Y. Gupta, A. and Apley, D. (2004) Singularity of fixture fault diagnosis in multi-station assembly systems. ASME Transactions, Journal of Manufacturing Science and Engineering, 126(1), 200-210.
22. Ding, Y., Jin, J., Ceglarek, D. and Shi, J. (2005) Process-oriented tolerancing for multi-station assembly systems. IIE Transactions, 37(6), 493-508.
23. Ding, Y., Shi, J. and Ceglarek,D. (2002) Diagnosability analysis ofmultistation manufacturing processes. ASME Transactions, Journal of Dynamic Systems, Measurement, and Control, 124(1), 1-13.
24. Ding,Y., Zhou, S. and Chen,Y. (2005)Acomparison of process variation estimators for in-process dimensional measurements and control. ASMETransactions, Journal of Dynamic Systems,Measurement and Control, 127(1), 69-79.
25. Djurdjanovic, D. and Ni, J. (2001) Linear state space modeling of dimensional machining errors. Transactions of NAMRI/SME, 29, 541-548.
26. Djurdjanovic, D. and Ni, J. (2007) On-line stochastic control of dimensional quality in multi-station manufacturing systems. Journal of Engineering Manufacture, 221(B5), 865-880.
27. Fenner, J.S., Jeong, M.K. and Lu, JC (2005) Optimal automatic control of multistage production processes. IEEE Transaction on Semiconductor Manufacturing, 18(1), 94-103.
28. Forrester, J.W. (1961) Industrial Dynamics, MIT Press, Cambridge, MA.
29. Gunasekaran, A., Goyal, S.K., Martikainen, T. and Yli-Olli, P. (1998) Production capacity planning and control in multi-stage manufacturing. The Journal of the Operational Research Society, 49(6), 625-634.
30. Hauck, D.J., Runger, G.C. and Montgomery, D.C. (1999) Multivariate statistical process monitoring and diagnosis with grouped regression-adjusted variables. Communications in Statistics. Simulation and Computation, 28(2), 309-328.
31. Hawkins, D.M. (1991) Multivariate quality control based on regressionadjusted variables. Technometrics, 33(1), 61-75.
32. Hawkins, D.M. (1993)Regression adjustment for variables inmultivariate quality control. Journal of Quality Technology, 25(3), 170-182.
33. Huang, Q., Zhou, N. and Shi, J. (2000) Stream of variation modeling and diagnosis of multi-station machining processes, in Proceedings of the 2000 ASME International Mechanical Engineering Congress & Exposition, Orlando, FL, Volume 11, pp. 81-88.
34. Huang, W. and Ceglarek, D. (2004) Tolerance analysis for design of multistage manufacturing processes using number-theoretical net method (NT-net). International Journal of Flexible Manufacturing Systems, 16(1), 65-90.
35. Huang, W., Lin, J., Bezdecny M., Kong, Z., and Ceglarek, D. (2007) Stream-of-variation modeling I: A generic 3D variation model for rigid body assembly in single station assembly processes. ASME Transactions, Journal of Manufacturing Science and Engineering, 129(4), 821-831.
36. Huang,W., Lin, J.,Kong, Z. and Ceglarek,D. (2007) Stream-of-variation (SOVA) modeling II: A generic 3D variation model for rigid body assembly in multi station assembly processes. ASME Transactions, Journal of Manufacturing Science and Engineering, 129(4), 832-842.
37. Huang, W., Phoomboplab, T. and Ceglarek, D. (2009) Process capability surrogate model-based tolerance synthesis for multistation manufacturing systems. IIE Transactions, 41(4), 309-322. Izquierdo, L.E., Shi J., Hu J., and Wampler, C.W. (2007) Feedforward control of multistage assembly processes using programmable tooling. Transactions of NAMRI/SME, 35, 295-303.
38. Jin, J. and Shi, J. (1999) State space modeling of sheet metal assembly for dimensional control. ASMETransactions, Journal of Manufacturing Science and Engineering, 121(4), 756-762.
39. Jin, N. and Zhou, S. (2006a) Data-driven variation source identification of manufacturing processes based on eigenspace comparison. Naval Research Logistics, 53(5), 383-396.
40. Jin, N. and Zhou, S. (2006b) Signature construction and matching for fault diagnosis in manufacturing processes through fault space analysis. IIE Transactions, 38(4), 341-354.
41. Johnson R.A. andWichern, D.W. (2002) Applied Multivariate Statistical Analysis, fifth edition, Prentice Hall, Upper Saddle River, NJ.
42. Kim, P. and Ding, Y. (2004) Optimal design of fixture layout in multistation assembly processes. IEEE Transactions on Automation Science and Engineering, 1(2), 133-145.
43. Kim, P. and Ding, Y. (2005) Optimal engineering design guided by datamining methods. Technometrics, 47(3), 336-348.
44. Kong, Z., Ceglarek, D. and Huang, W. (2008) Multiple fault diagnosis method in multi-station assembly processes using orthogonal diagonalization analysis. ASME Transactions, Journal of Manufacturing Science and Engineering, 130(1), No. 011014.
45. Kourti, T. and MacGregor, J.F. (1996) Multivariate SPC methods for process and product monitoring. Journal of Quality Technology, 28(4), 409-428.
46. Krzanoski, W.J. (1979) Between-groups comparison of principal components. Journal of the American Statistical Association, 74 367), 703-707.
47. Lawless, J.F., Mackay, R.J. and Robinson, J.A. (1999) Analysis of variation transmission in manufacturing processes - part I. Journal of Quality Technology, 31(2), 131-142.
48. Li, J. and Shi, J. (2007) Knowledge discovery from observational data for process control through causal Bayesian networks. IIE Transactions, 39(6), 681-690.
49. Li,Y. and Tsung, F. (2009)False discovery rate-adjusted charting schemes for multistage process fault diagnosis and isolation. Technometrics 51(2), 186-205.
50. Li, Z. and Zhou, S. (2006) Robust method of multiple variation sources identification in manufacturing processes for quality improvement. ASME Transactions, Journal of Manufacturing Science and Engineering, 128(1), 326-336.
51. Li, Z., Zhou, S. and Ding, Y. (2007) Patternmatching for root cause identification of manufacturing processes with consideration of general structured noise. IIE Transactions, 39(3), 251-263.
52. Liu, J., Jin, J. and Shi, J. (2009) State space modeling for 3-dimensional variation propagation in rigid-body multistage assembly processes. IEEE Transactions on Automation Science and Engineering to appear.
53. Liu, L., Liu, X. and Yao, D.D. (2004) Analysis and optimization of a multistage inventory-queue system. Management Science, 50 3), 365-380.
54. Liu, J., Shi, J. and Hu, S.J. (2008) Engineering-driven factor analysis for variation sources identification in multistage manufacturing processes. ASME Transactions, Journal of Manufacturing Science and Engineering, 130(4), No. 041009.
55. Liu, J., Shi, J. and Hu, S.J. (2009) Quality assured setup planning based on the stream of variation model for multi-stage machining processes. IIE Transactions, 41(4), 323-334.
56. Loose, J., Zhou, S. and Ceglarek, D. (2007) Kinematic analysis of dimensional variation propagation formultistage machining processes with general fixture layouts. IEEE Transactions on Automation Science and Engineering, 4(2), 141-152.
57. Loose, J., Zhou, S. and Ceglarek, D. (2008) Variation source identification in manufacturing processes based on relational measurements of key product characteristics. ASME Transactions, Journal of Manufacturing Science and Engineering, 130(3), No. 031007.
58. Loose, J., Zhou, Q., Zhou, S. and Ceglarek, D. (2009) Integrating GD&T into dimensional variation models for multistage machining processes. International Journal of Production Research, to appear.
59. Madou, M.J., (2002), Fundamentals of Microfabrication, the Science of Miniturization, second edition, CRC Press, New York, NY.
60. Mandel, B.J. (1969) The regression control chart. Journal of Quality Technology, 1(1), 1-9.
61. Mandroli, S.S., Shrivastava,A. and Ding,Y. (2006)Asurvey of inspection strategy and sensor distribution studies in discrete-partmanufacturing processes. IIE Transactions, 38(4), 309-328.
62. Mantripragada, R. and Whitney, D.E. (1999) Modeling and controlling variation propagation in mechanical assemblies using state transition models. IEEE Transactions on Robotics and Automation, 15(1 , 124-140.
63. McCulloch, C. and Searle, S.R. (2001) Generalized, Linear, and Mixed Models, Wiley, New York, NY.
64. Montgomery, D.C. and Woodall, W.H. (Eds.) (1997) A discussion on statistically-based process monitoring and control. Journal of Quality Technology, 29(2), 121-162.
65. Nomikos, P. and MacGregor, J.F. (1995) Multivariate SPC charts for monitoring batch processes. Technometrics, 37(1), 41-59.
66. Phoomboplab, T. and Ceglarek, D. (2007) Design synthesis framework for dimensionalmanagement inmulti-station assembly systems. Annals of CIRP, 56(1), 153-158.
67. Phoomboplab, T. and Ceglarek, D. (2008) Process yield improvement through optimal design of fixture layout in 3D multi-station assembly systems. ASME Transactions, Journal of Manufacturing Science and Engineering, 130(6), No. 061005.
68. Raman, K.N., Strojwas, A.J., Maly,W.P., Ouyang, C., Shindo, W., Akella, R., McIntyre, M.G. and Derrett, J. (1998) In-line yield prediction methodologies using patterned wafer inspection information. IEEE Transactions on Semiconductor Manufacturing, 11(1), 40-47.
69. Ren, Y., Ding, Y. and Zhou, S. (2006) A data-mining approach to study the significance of nonlinearity in multi-station assembly processes. IIE Transactions, 38(12), 1069-1083.
70. Rong, Q., Ceglarek, D. and Shi, J. (2000) Dimensional fault diagnosis for compliant beam structure assemblies. ASME Transactions, Journal of Manufacturing Science and Engineering, 122(4), 773-780.
71. Sawik, T.J. (1987) Multilevel scheduling of multistage production with limited in-process inventory. The Journal of the OperationalResearch Society, 38(7), 651-664.
72. Searle, S.R., Casella, G. and McCulloch, C.E. (1992) Variance Components, New York, NY.
73. Shan, X. and Apley, D.W. (2008) Blind identification of manufacturing variation patterns by combining source separation criteria. Technometrics, 50(3), 332-343.
74. Shi, J. (2007) Stream of Variation Modeling and Analysis for Multistage Manufacturing Processes, CRC Press, Boca Raton, FL.
75. Shindo, W., Raman, K.N., and Strohwas, A.J. (1998) Effects of defect propagation/growth on in-line defect-based yield prediction. IEEE Transactions on Semiconductor Manufacturing, 11(4), 546-551.
76. Suri, R. and Otto, K. (1999) Variation modeling for a sheet stretch forming manufacturing system. Annals of CIRP, 48, 397-400.
77. Undey, C. and Cinar, A. (2002) Statistical monitoring of multistage, multiphase batch processes. IEEE Control System Magazine, 22 5), 40-52.
78. Wade, M.R. and Woodall, W.H. (1993) A review and analysis of causeselecting control charts. Journal of Quality Technology, 25(3), 161-169.
79. Xiang, L. and Tsung, F. (2008) Statistical monitoring of multistage processes based on engineering models. IIE Transactions, 40 10), 957-970.
80. Xie, K, Wells, L., Camelio, J. and Youn, B.D. (2007) Variation propagation analysis on compliant assemblies considering contact interaction. ASME Transactions, Journal of Manufacturing Science and Engineering, 129(1), 934-942.
81. Zantek, P.F., Wright, G.P. and Plante, R.D. (2002) Process and product improvement in manufacturing systems with correlated stages. Management Science, 48(5), 591-606.
82. Zhang, G.X. (1985) Cause-selecting control charts: A new type of quality control charts. The QR Journal, 12, 221-225.
83. Zeng, L., Jin, N. and Zhou, S. (2008) Multiple fault signature integration and enhancing for variation source identification in manufacturing processes. IIE Transactions, 40(10), 919-930.
84. Zeng, L. and Zhou, S. (2007) Inferring the interactions in complex manufacturing processes using graphical models. Technometrics, 49(4), 373-381.
85. Zeng, L. and Zhou, S. (2008) Impacts of measurement errors and regressor selection on regression adjustment monitoring of multistage manufacturing processes. IIE Transactions, 40(2), 109-121.
86. Zhang, M., Djurdjanovic, D., and Ni, J. (2007) Diagnosibility and sensitivity analysis for multi-station machining processes. International Journal of Machine Tools and Manufacture, 47(3), 646-657.
87. Zhou, S., Chen, Y. and Shi, J. (2004) Root cause estimation and statistical testing for quality improvement of multistage manufacturing processes. IEEE Transactions on Automation Science and Engineering 1(1), 73-83.
88. Zhou, S., Ding, Y., Chen Y. and Shi, J. (2003b) Diagnosability study of multi-station manufacturing processes based on linearmixed model. Technometrics, 45(4), 312-325.
89. Zhou, S., Huang, Q. and Shi, J. (2003) State space modeling of dimensional variation propagation in multistage machining process using differential motion vectors. IEEE Transactions on Robotics and Automation, 19(2), 296-309.
90. Zou, C. and Tsung, F. (2008) Directional MEWMA schemes for multistage process monitoring and diagnosis. Journal of Quality Technology, 40(4), 407-427.
91. Zou, C., Tsung, F. and Liu, Y. (2008) A change point approach for phase I analysis in multistage processes. Technometrics, 50(3), 344-356.